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Statistical Signal Processing
Lennart, L. (1999). System identification: theory for the user. PTR Prentice Hall, Upper Saddle River, NJ, 1-14. This page contains resources about Statistical Signal Processing, Point Estimation, Estimation Theory, Adaptive Filtering, Adaptive Signal Processing and Adaptive Filter Theory, and System Identification. Subfields * Least mean squares (LMS) filter * Kernel least mean squares (KLMS) filter * Recursive least squares (RLS) filter * Normalised least mean squares (NLMS) filter * Hierarchical least mean squares (HLMS) filter * Complex valued filters ** Complext least mean squares (CLMS) filter * Square root filter * Monte Carlo Methods * Blind Deconvolution * Artificial Neural Networks ** Back-Propagation Algorithm * Bayesian point-estimation / Bayesian Parameter Estimation ** Wiener filter ** Bayes filter / Recursive Bayesian estimation *** Kalman filter ** Maximum a posteriori (MAP) *** Maximum likelihood estimation (MLE) * Methods for finding estimators ** Method of Moments ** Maximum Likelihood Estimator ** Bayes Estimator ** Expectation-Maximization Algorithm ** Minimum-variance mean-unbiased estimator (MVUE) ** Median-unbiased estimator * Methods for evaluating estimators ** Minimum mean squared error (MMSE) / Bayes least squared error (BLSE) ** Best linear unbiased estimator (BLUE) * M-estimators ** MLE ** Steepest Descent / Gradient Descent *** Stochastic Gradient Descent (SGD) *** Generalised Normalised Gradient Descent (GNGD) *** Hierarchical gradient descent (HGD) * Cramér–Rao bound * Black-box and grey-box models * Parametric and nonparametric models * Time series models * State Space Models ** Subspace Identification *** N4ASID *** MOESP *** CVA / CCA *** SSARX * Time domain methods * Frequency domain methods / Spectral Estimation ** Empirical transfer function estimation (ETFE) ** Periodogram * Minimum message length (Occam's Razor) * Adaptive control ** Auto-tuning Online Courses Video Lectures * Adaptive filters by Ali H. Sayed Lecture Notes * Books and Book Chapters * Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. * Simon, H. (2014). Adaptive filter theory. 5th Ed. Prentice Hall. * Tangirala, A. K. (2014). Principles of System Identification: Theory and Practice. CRC Press. * Van Trees, H. L. (2013). Detection Estimation and Modulation Theory, , Part I: Detection, Estimation, and Filtering Theory. 2nd Ed. John Wiley & Sons. * Sayed, A. H. (2011). Adaptive filters. John Wiley & Sons. * Hayes, M. H. (2009). Statistical digital signal processing and modeling. John Wiley & Sons. * Mandic, D. P., & Goh, V. S. L. (2009). Complex valued nonlinear adaptive filters: noncircularity, widely linear and neural models (Vol. 59). John Wiley & Sons. * Goodwin, G. C., & Sin, K. S. (2014). Adaptive filtering prediction and control. Dover Publications. * Åström, K. J., & Wittenmark, B. (2013). Adaptive control. Dover Publications. * Van den Bos, A. (2007). Parameter estimation for scientists and engineers. John Wiley & Sons. * Poularikas, A. D., & Ramadan, Z. M. (2006). Adaptive filtering primer with MATLAB. CRC Press. * Gray, R. M., & Davisson, L. D. (2004). An introduction to statistical signal processing. Cambridge University Press. * Cichocki, A., & Amari, S. I. (2002). Adaptive blind signal and image processing: learning algorithms and applications. John Wiley & Sons. * Kay, S. M. (1993). Fundamentals of Statistical Signal Processing, Vol I: Estimation Theory. * Lennart, L. (1999). System identification: theory for the user. PTR Prentice Hall, Upper Saddle River, NJ. * Van Overschee, P., & De Moor, B. L. (1996). Subspace identification for linear systems: Theory—Implementation—Applications. Springer Science & Business Media. Scholarly Articles * De Cock, K., De Moor, B., & Leuven, K. U. (2003). Subspace identification methods. Contribution to section, 5, 933-979. Software * System Identification Toolbox - MATLAB * See also * Stochastic Processes * Optimization Other Resources Category:Signal Processing Category:Probability and Statistics Category:Control Theory Category:Machine Learning